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\title{Automatic License Plate Recognition (ALPR) for Bangladeshi Vehicles}
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\begin{document}

             \author[1]{Ashim Kumar  Ghosh}

             \author[2]{Swapan K.D.  Sharma}

             \author[3]{Md. Nazrul  Islam}

             \affil[1]{  Dhaka University Of Engineering and Technology, Gazipur-1700.}

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\date{\small \em Received: 24 October 2011 Accepted: 21 November 2011 Published: 4 December 2011}

\maketitle


\begin{abstract}
        


This paper presents Automatic License Plate extraction, character segmentation and  recognition method for license plate of Bangladeshi vehicles with chain code and neural network.  In Bangladesh, license plate models are not followed strictly. Characters on plate are in Bangla  and English languages and also are in one or two lines. Due to dissimilarity in the model of  license plates, vehicle license plate extraction, character segmentation and recognition are key  issue. Different types of algorithm already applied and the performance is examined for English  license plate. We describe the license plate extraction, character segmentation and recognition  work, with Bangla characters. License plate extraction is performed using Sobel filter, connected  component analysis and morphological operations. Character segmentation is performed in  different levels by using scanning the binary image horizontally and vertically and connected  component analysis. Character recognition is carried out using chain code generation and  stored knowledge of the network.   

\end{abstract}


\keywords{Vehicle license plate, line segmentation, word segmentation, character recognition, chain code, neural network.}

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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
utomatic License Plate Recognition (ALPR) is an important area of research due to its applications. It is a machine vision technique used to identify vehicles by their license plates without direct human involvement. The Intelligent Transportation System provides the data of vehicle information which can be used in follow up, analysis and monitoring. The complexity of automatic license plate recognition work varies throughout the world. For the standard license plate, ALPR system is easier to read and recognize. In Bangladesh this task becomes much difficult due to variation in plate model and specialties of Bangla scripts.\par
Bangla text can be partitioned into three zones. The upper zone indicate the portion above the headline called 'Marta', the middle zone indicate the portion of basic characters and compound characters under the head-line and lower zone is the portion where some of the modifiers can exist in .The ALPR algorithm consists of three steps: license plate locating as well as true license plate extraction, character segmentation and character recognition. From the input image, the license plate detection, noise removal, invert and skew detection is performed from license plate in license plate extraction phase. In character segmentation phase each and every character is isolated and segmented accordingly followings step line segmentation, word segmentation and character segmentation, based on the selection of good features of characters, each character is recognized using neural network, in the character recognition phase. Extraction of license plate is difficult job; mainly due to license plate occupy a small part of whole image, difference in license plate models and cause of environmental factors. This step affects the accuracy of character segmentation and recognition work.\par
The rest of this paper is prepared as follows segment II shortly illustrates the applications of the ALPR system in different areas, Segment III described related works, Segment IV described true license plate extraction process, Segment V includes steps of the character segmentation and Segment VI includes steps character recognition process. Segment VII discusses its experimental results and lastly Segment VIII concludes the paper. II. 
\section[{APPLICATION}]{APPLICATION}\par
ALPR system is mostly used in Intelligent Transportation System. ALPR is important in the area of highway toll collection, traffic problems, borders and custom security, premises where high security is needed, like national assembly, V.I.P houses and so on. plate extraction. Hao Chen \hyperref[b0]{[1]} et al planned the method, several candidates based on texture information similar plate detection technique using group of lines forming rectangle at the plate boundary, followed by this step is the vertical edge density technique to find out the plate area. Ozbay et al \hyperref[b2]{[3]} developed smearing algorithm to locate the license plate. Mei Yu et al \hyperref[b3]{[4]} proposed vertical edge detection followed by size, shape filter for edge area and edge matching technique based on plate model. Farhad Faradji et al \hyperref[b4]{[5]} first used Sobel edge detection on the image. Next, vertical projection analysis was used to locate plate area. False candidates were removed using compact factor, which estimated the densest vertical edge area declaring true license plate. Every character on detected license plates is segmented in character segmentation step. Segmentation techniques based on projection analysis, Hough transform, region growing are proposed in the text. Xinagjian He et al \hyperref[b5]{[6]} used horizontal and vertical projection analysis for character segmentation. Yuangang Zhang et al \hyperref[b6]{[7]} developed character segmentation using Hough Transform. In this, horizontal edges of the plate area were decided initially, using Hough Transform, which helped to segment the characters with the large rotation. Characters were segmented using vertical projection analysis based on the prior knowledge of the plate model. Feng Yang et al \hyperref[b7]{[8]} developed region growing algorithm for character segmentation. Shen Zheng Wang et al \hyperref[b8]{[9]} used connected component analysis for character segmentation. 
\section[{III.}]{III.} 
\section[{RELATED WORK}]{RELATED WORK}\par
In this paper ALPR work for Bangladeshi car is presented. Images are taken out with different lighting conditions, different background and direction. Histogram equalization, median filter are used which take care of lighting and contrast problem. Sobel vertical edge detection and morphology is employed to locate the license plate. Horizontal and vertical scanning is used to segment the line, word and characters. For character recognition work chain code and neural network is used.\par
IV. 
\section[{LICENSE PLATE EXTRACTION}]{LICENSE PLATE EXTRACTION}\par
License plate extraction is the key step in ALPR system, which maintains the accuracy of the system significantly. The goal of this phase, given an input image, is to produce a region that contains true license plate. 
\section[{a) Image capturing and noise removing}]{a) Image capturing and noise removing}\par
In this system a high resolution digital camera is used to capture an image. Images are taken in different background, illumination conditions and at various distances from the camera to vehicle. Images are resized to (1024 X 768). All the processing steps are executed on gray scale image. Preprocessing is mainly used to enhance the processing speed, improve the contrast of the image and to reduce the noise in the image. Therefore, it is necessary to filter this noise before we process the image. Usually the low-pass filter approach is used to reduce the problem of low quality and low contrast in vehicle images. Original image that captured by digital camera and corresponding gray scale image are showed in Fig. \hyperref[fig_1]{1}  The license plate region contains plentiful edges with respect to background. Sobel edge detection is used to find out the regions which have high pixel variance value. To extract candidate license plate area from the entire image, threshold is used to select rows which are having particular white pixel density. Fig. \hyperref[fig_2]{2} shows the result of effect of using, Sobel edge detection and threshold \hyperref[b9]{[10]}. Morphological operations aim to remove unrelated objects in the image. Dilation and erosion are used to extract candidate plate areas from the entire image. Sometimes background areas may also get declared as candidate plate. Hence to remove the fake candidates, plate validation is done using the aspect ratio of the plate and horizontal cuts in the license plate \hyperref[b10]{[11]}. Invert and threshold operation is performed for true license plate extraction. The captured image may be skewed, so skew detection and correction necessary to make text lines to license plate are extracted and auto-correlation based binary image and projection algorithm are used to verify the true candidate plate. Gisu Heo \hyperref[b1]{[2]} developed license horizontal. Skew angle makes the text lines of the document image with the horizontal direction. Skew correction can be accomplished in two steps. Firstly, we will estimate the skew angle and secondly, we will rotate the image by skew angle.   
\section[{CHARACTER SEGMENTATION}]{CHARACTER SEGMENTATION}\par
Character isolation from the license plate region is very important and crucial step in ALPR system, which influences the accuracy of character recognition significantly. The goal of this phase, given the license plate image, is to segment all the characters, without loosing features of the characters. This phase consists of the sequence of operations as, line segmentation, word segmentation, character segment and connected component analysis.  Each line is scanned vertically for word segmentation. Number of black pixels in each column is calculated to construct column histogram. The portion of the line with continuous black pixels is considered to be a word in that line. If no black pixel is found in some vertical scan that is considered as the spacing between words. Thus different words in different lines are separated. So the image file can now be considered as a collection of words. Fig. \hyperref[fig_8]{7} shows the word segmentation process.   Detection of character between headline removing the headline the characters in a word are isolated and can easily be separated. Vertical scan is initiated from the row that is just beneath the 'Matra' row to find the differentiation line between characters. If during scan, one can reach the base line without touching any black pixel then this scan successfully found a differentiation line between characters. Fig. \hyperref[fig_10]{9} illustrates the character segmentation process.   For a closed boundary, its chain code obviously depends on the starting point of the boundary. To make it invariant to the starting point, the chain code can be normalized according to the following method \hyperref[b12]{[13]}: A chain code can be treated as a circular sequence of direction numbers. Therefore, the starting point can be redefined so that the resulting sequence of numbers forms a minimum integer. A neural network \hyperref[b13]{[14]} is a extremely parallel distributed processor that has a propensity for storing knowledge. First knowledge is acquired by the network through a learning process and then storing knowledge is for recognition of character. We use feed forward neural network for the classification and recognition Bangla characters. We trained the neural network by normalized feature vector obtained for each character in the training set. Our layer neural networks have been used with two hidden layers for improving the classification capability. For 32 dimensional feature vectors and 4 layers is used for each Character and recognized using stored knowledge of the network. Use of two hidden layers increases the recognition rate extensively.  
\section[{CONCLUSIONS}]{CONCLUSIONS}\par
An algorithm for vehicle License plate extraction, character segmentation and recognition is offered. Experiment consists of images with different size, lighting, background, camera angle and distance etc. The experimental results show that, license plates are extracted truly with higher success. Character recognition phase using connected component analysis, Freeman chain code, and Neural network that works well. We suggest to use multilayer feed forward neural network for classify and recognition of character. Recognition performance will be increased if the network trains with distorted character and with good shaped character.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
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\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 1 :}\end{figure}
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\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 2 :}\end{figure}
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\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 3 :}\end{figure}
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\noindent\textbf{4}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Fig. 4 :}\end{figure}
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\noindent\textbf{5}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Fig. 5 :}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{fig_6}}\end{figure}
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\noindent\textbf{6}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Fig. 6 :}\end{figure}
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\noindent\textbf{7}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Fig. 7 :}\end{figure}
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\noindent\textbf{8}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Fig. 8 :}\end{figure}
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\noindent\textbf{9}\includegraphics[]{image-12.png}
\caption{\label{fig_10}Fig. 9 :}\end{figure}
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\noindent\textbf{10}\includegraphics[]{image-13.png}
\caption{\label{fig_11}Fig. 10 :}\end{figure}
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\noindent\textbf{11}\includegraphics[]{image-14.png}
\caption{\label{fig_12}Fig. 11 :}\end{figure}
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\noindent\textbf{12}\includegraphics[]{image-15.png}
\caption{\label{fig_13}Fig. 12 :}\end{figure}
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\noindent\textbf{13}\includegraphics[]{image-16.png}
\caption{\label{fig_14}Fig. 13 :}\end{figure}
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\noindent\textbf{14}\includegraphics[]{image-17.png}
\caption{\label{fig_15}Fig. 14 :}\end{figure}
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\noindent\textbf{} \par 
\begin{longtable}{P{0.5692660550458716\textwidth}P{0.12217125382262996\textwidth}P{0.11957186544342507\textwidth}P{0.0389908256880734\textwidth}}
\tabcellsep ?\tabcellsep , Sameya Akter\tabcellsep ¥\\
\tabcellsep \tabcellsep \tabcellsep 2011\\
\tabcellsep \tabcellsep \tabcellsep December\\
\tabcellsep \tabcellsep \tabcellsep 69\\
A\tabcellsep \tabcellsep \\
?\tabcellsep \tabcellsep \\
Author ¥ : B.Sc. in engineering degree in Computer Science and\tabcellsep \multicolumn{3}{l}{Different techniques are developed for license}\\
Engineering (CSE) from Dhaka University of Engineering and\tabcellsep \tabcellsep \\
Technology (DUET), Gazipur-1700, Bangladesh.\tabcellsep \tabcellsep \\
Email : sameyaakter@yahoo.com , PH : +88 01726133150.\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{© 2011 Global Journals Inc. (US)}\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 			\footnote{© 2011 Global Journals Inc. (US) Global Journal of Computer Science and Technology Volume XI Issue XXI Version I} 			\footnote{DecemberAutomatic License Plate Recognition (ALPR) for Bangladeshi Vehicles} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Faradji et al. ()]{b4}\label{b4} 	 		‘A Morphological based License Plate Location’.  		 			Farhad Faradji 		,  		 			Majid Amir Hossein Rezaie 		,  		 			Ziaratban 		.  	 	 		\textit{ICIP}  		2007. p. .  	 
\bibitem[Zhang and Zhang ()]{b6}\label{b6} 	 		‘A New algorithm for character segmentation of license plate’.  		 			Yungang Zhang 		,  		 			Changshui Zhang 		.  	 	 		\textit{Proc. Of IEEE Intelligent Vehicles Symposium},  				 (Of IEEE Intelligent Vehicles Symposium)  		2003. p. .  	 
\bibitem[Yang et al. ()]{b7}\label{b7} 	 		‘A Novel approach for license plate character segmentation’.  		 			Feng Yang 		,  		 			Zheng Ma 		,  		 			Mei Xie 		.  	 	 		\textit{ICIEA}  		2006. p. .  	 
\bibitem[Hao Chen et al. ()]{b0}\label{b0} 	 		‘A novel method for license plate localization’.  		 			Jisheng Hao Chen 		,  		 			Huachun Ren 		,  		 			Jianqun Tan 		,  		 			Wang 		.  	 	 		\textit{4th Proc. of ICIG},  				2007. p. .  	 
\bibitem[Yu and Kim ()]{b3}\label{b3} 	 		‘An approach to Korean license plate recognition based on vertical edge matching’.  		 			Mei Yu 		,  		 			Yong Deak Kim 		.  	 	 		\textit{IEEE International Conference on System, Man and Cybernetics},  				2000. 4 p. .  	 
\bibitem[Ozbay and Ercelebi ()]{b2}\label{b2} 	 		‘Automatic vehicle identification by plate recognition’.  		 			Serkan Ozbay 		,  		 			Ergun Ercelebi 		.  	 	 		\textit{Proc. of PWASET},  				 (of PWASET)  		2005. 9 p. .  	 
\bibitem[Tran Dur Duan et al.]{b10}\label{b10} 	 		‘Combining Hough transform and contour algorithm for detecting vehicle license plate’.  		 			Tran Dur Duan 		,  		 			Anh Duong 		,  		 			Tran Duc 		,  		 			Hong Le 		,  		 			Du 		.  	 	 		\textit{Proc. Of International},  				 (Of International)  		 	 
\bibitem[Freeman ()]{b12}\label{b12} 	 		‘Computer processing of line drawing images’.  		 			H Freeman 		.  	 	 		\textit{Computer Survey}  		1974. 6 p. .  	 
\bibitem[Shen-Zheng et al. ()]{b8}\label{b8} 	 		‘Detection and recognition of license plate characters with different appearances’.  		 			Shen-Zheng 		,  		 			His-Jian Wang 		,  		 			Lee 		.  	 	 		\textit{Proc. of 16 th International Conference on Pattern Recognition},  				 (of 16 th International Conference on Pattern Recognition)  		2003. 3 p. .  	 
\bibitem[Rafel et al. ()]{b9}\label{b9} 	 		\textit{Digital Image Processing},  		 			C Rafel 		,  		 			Richard E Gonzalez 		,  		 			Woods 		.  		2002. Prentice Hall, Inc.  	 	 (2nd edition) 
\bibitem[Heo et al. ()]{b1}\label{b1} 	 		‘Extraction of car license plate regions using line grouping and edge density methods’.  		 			Gisu Heo 		,  		 			Minwoo Kim 		,  		 			Insook Jung 		.  	 	 		\textit{International Symposium on Information Technology Convergence},  				2007. p. .  	 	 (Duk Ryong Lee,Il Seok Oh) 
\bibitem[Gonzalez and Woods ()]{b13}\label{b13} 	 		 			R Gonzalez 		,  		 			R E Woods 		.  		\textit{Digital Image Processing},  				2002. Prentice Hall.  	 
\bibitem[Haykin]{b14}\label{b14} 	 		 			S Haykin 		.  		\textit{Neural Networks, A Comprehensive Foundation},  				 	 	 (2nd Edition) 
\bibitem[He (2008)]{b5}\label{b5} 	 		‘Segmentation of characters on car license plates’.  		 			Xiangjian He 		.  	 	 		\textit{10 th Workshop on Multimedia Signal Processing},  				Oct. 2008. p. .  	 
\bibitem[Symposium Intelligent Multimedia, Video and Speech Processing ()]{b11}\label{b11} 	 		\textit{Symposium Intelligent Multimedia, Video and Speech Processing},  				2004. p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
